| Description: |
AI Engineer
Building production-grade AI that actually ships This is not an R&D lab role. This is about taking AI out of experimentation and embedding it into real products and services that people rely on. We're looking for an AI Engineer who enjoys building things that run in production, scale with demand, and hold up under real-world usage. You'll design, build and operate AI systems end to end, working in a modern, mesh-style environment where data is owned by domains and AI capabilities are integrated into broader software platforms. If you care about reliability as much as model performance, and you like seeing your work live in real products, this role is built for you. The role
You'll work across the full AI life cycle, from data ingestion and model development through deployment, monitoring and ongoing operation. You won't just train models; you'll make them reliable, observable and usable in production. Operating in a distributed data landscape, you'll design AI systems that consume domain-owned data products and integrate seamlessly into user-facing applications and platforms. You'll collaborate closely with software engineers, product teams and data owners to ensure AI is a first-class, production-ready capability. What you'll be doing -
Developing, deploying and operating AI models and AI-powered services in live environments -
Building end-to-end pipelines covering data ingestion, training, inference and monitoring -
Designing AI systems that consume data products across a distributed, mesh-style landscape -
Integrating AI capabilities into software platforms and customer-facing applications -
Monitoring model behaviour, performance, cost and failure modes in production -
Contributing to shared AI tooling, standards and technical direction across the organisation What success looks like -
AI systems are reliable, explainable and genuinely usable in real-world environments -
Models remain robust as data sources, domains and usage patterns evolve -
Trade-offs between accuracy, latency, cost and risk are clearly understood and actively managed -
AI moves from interesting to essential within the products it supports Experience and capabilities -
Strong Python development skills -
Proven experience building and deploying AI or machine learning systems into production -
Familiarity with cloud-based AI services and modern MLOps practices -
Understanding of data mesh principles, including distributed ownership and federated governance -
Solid engineering fundamentals including testing, version control and CI/CD Desirable experience -
Hands-on experience with LLM-based systems or agent-style architectures -
Familiarity with AI observability, evaluation and monitoring techniques -
Experience working in cross-functional, product-led teams Why this role
You'll be trusted to build AI that matters, not prototypes that never leave the lab. You'll have the chance to shape how AI is designed, deployed and governed across a distributed environment, and to leave behind systems that are measurable, maintainable and impactful. If you enjoy combining strong engineering discipline with modern AI, and you want your work to show up in real products, this is an opportunity to do exactly that.  |
